A systems genomics approach uncovers molecular associates of RSV severity

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

PyLiger: Scalable single-cell multi-omic data integration in Python

Motivation: LIGER is a widely-used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly-optimized scientific computing libraries for increased efficiency. Results: We developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2-5× speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability, and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory.

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics as well as for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms existing state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, non-linear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared to five widely used binning algorithms, binny recovers the most near-complete (>95% pure, >90% complete) and high-quality (>90% pure, >70% complete) genomes from simulated data sets from the Critical Assessment of Metagenome Interpretation (CAMI) initiative, as well as from a real-world benchmark comprised of metagenomes from various environments. binny is implemented as Snakemake workflow and available from https://github.com/a-h-b/binny.

TAMI-76. INTEGRATED MULTI-OMICS REVEAL INTRATUMOUR HETEROGENEITY AND NOVEL THERAPEUTIC TARGETS IN PAEDIATRIC EPENDYMOMA

Ependymoma (EPN) is the second most common malignant paediatric brain tumour with a five-year survival rate of only 25% following relapse. While molecular heterogeneity between EPN tumours is well understood, little is known concerning spatially-distinct intratumour heterogeneity within patients. In this context, we present a multi-omics integration of expression data at transcriptomic and metabolomic levels revealing intratumour heterogeneity and novel therapeutic targets. Surgically resected ependymoma tissue from two epigenetic subgroups, posterior fossa-A (PF-A) and supratentorial RELA, were first homogenised and polar metabolites, lipids and RNA simultaneously extracted from the same cellular population. Using liquid chromatography-mass spectrometry (LC-MS) and RNAseq 115 metabolites and 1580 upregulated genes were identified between the two subgroups, therefore validating previously reported genetic clustering of these two subtypes. Sampling of anatomically distinct regions was performed between eight PF-A EPN patients and multi-omic data was compared across 28 intratumour regions, with at least 3 different regions per patient. Integration of genes and metabolites revealed 124 dysregulated metabolic pathways, encompassing 156 genes and 49 metabolites. A large number of interactions occur in the gluconeogenesis and glycine pathways in 6 out of 8 patients, putatively representing therapeutically relevant ubiquitous metabolic pathways critical for EPN survival. Each anatomical region also presented at least one unique gene-metabolite interaction demonstrating heterogeneity within and across PF-A EPN tumours. A subset of the eight most prevalent genes across patients (GAD1, NT5C, FBP1, FMO3, HK3, TALDO1, NT5E, ALDH3A1) were selected for in vitro metabolic assays using 10 repurposed cytotoxic agents against PF-A EPN cell lines derived from intratumour regions of the same patient. 5/8 genes map within the gluconeogenesis metabolic pathway, further highlighting its significance within PF-A EPN. This is the first instance where multi-omic data integration and intratumour heterogeneity has been investigated for paediatric EPN revealing novel potential targets in the context of gene-metabolite correlations.

The Cellular Composition of the Uveal Immune Environment

The uveal tract consists of the iris, the ciliary body and the choroid; these three distinct tissues form a continuous layer within the eye. Uveitis refers to inflammation of any region of the uveal tract. Despite being grouped together anatomically, the iris, ciliary body and choroid are distinct functionally, and inflammatory diseases may affect only one part and not the others. Cellular structure of tissues direct their function, and understanding the cellular basis of the immune environment of a tissue in health, the “steady state” on which the perturbations of disease are superimposed, is vital to understanding the pathogenesis of those diseases. A contemporary understanding of the immune system accepts that haematopoietic and yolk sac derived leukocytes, though vital, are not the only players of importance. An array of stromal cells, connective tissue cells such as fibroblasts and endothelial cells, may also have a role in the inflammatory reaction seen in several immune-mediated diseases. In this review we summarise what is known about the cellular composition of the uveal tract and the roles these disparate cell types have to play in immune homeostasis. We also discuss some unanswered questions surrounding the constituents of the resident leukocyte population of the different uveal tissues, and we look ahead to the new understanding that modern investigative techniques such as single cell transcriptomics, multi-omic data integration and highly-multiplexed imaging techniques may bring to the study of the uvea and uveitis, as they already have to other immune mediated inflammatory diseases.

A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis

Background There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

Synergistic effect of short- and long-read sequencing on functional meta-omics

Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artefacts from genes and proteins present in situ. Here, we evaluate short-read-only, long-read-only, and hybrid assembly approaches on four different metagenomic samples of varying complexity and demonstrate how they affect gene and protein inference which is particularly relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic, and metaproteomic data to evaluate the metagenomic data-based protein predictions. Our findings pave the way for critical assessments of metagenomic reconstructions and we propose a reference-independent solution based on the synergistic effects of multi-omic data integration for the in situ study of microbiomes using long-read sequencing data.

Results of the Seventh Scientific Workshop of ECCO: Precision medicine in IBD - disease outcome and response to therapy

Inflammatory bowel diseases [IBD] are a heterogeneous spectrum with two extreme phenotypes, Crohn’s disease [CD] and ulcerative colitis [UC], which both represent numerous phenotypical variations. Hence, we should no longer approach all IBD patients similarly, but rather aim to rethink clinical classifications and modify treatment algorithms to usher in a new era of precision medicine in IBD. This scientific ECCO workshop aims to provide a state-of-the-art overview on prognostic and predictive markers, shed light on key questions in biomarker development, propose best practices in IBD biomarker development [including trial design], and discuss the potential for multi-omic data integration to help drive further advances to make precision medicine a reality in IBD.